Geeking with Greg

Saturday, June 24, 2017

On its 20th anniversary, the editorial board created its first ever “The Test of Time” award. I'm honored to say they gave it to our 2003 article, "Amazon.com Recommendations: Item-to-Item Collaborative Filtering", which continues to be accessed, cited, and used in industry and research many years after its original publication.

For two decades now, Amazon.com has been building a store for every customer. Each person who comes to Amazon.com sees it differently ... It's as if you walked into a store and the shelves started rearranging themselves, with what you might want moving to the front, and what you're unlikely to be interested in shuffling further away.

Amazon.com launched item-based collaborative filtering in 1998, enabling recommendations at a previously unseen scale for millions of customers and a catalog of millions of items. Since we wrote about the algorithm in IEEE Internet Computing in 2003, it has seen widespread use across the Web, including YouTube, Netflix, and many others.

The algorithm's success has been from its simplicity, scalability, and often surprising and useful recommendations, as well as desirable properties such as updating immediately based on new information about a customer and being able to explain why it recommended something in a way that's easily understandable.

What was described in our 2003 IEEE Internet Computing article has faced many challenges and seen much development over the years ... We describe some of the updates, improvements, and adaptations for item-based collaborative filtering, and offer our view on what the future holds for collaborative filtering, recommender systems, and personalization.

....

What does the future hold for recommendations? ... Discovery should be like talking with a friend who knows you, knows what you like, works with you at every step, and anticipates your needs.

Recommendations and personalization live in the sea of data we all create as we move through the world, including what we find, what we discover, and what we love ... Intelligent computer algorithms leveraging collective human intelligence ... Computers helping people help other people.

The field remains wide open. An experience for every customer ... offering surprise and delight ... is a vision none have fully realized. Much opportunity remains to add intelligence and personalization to every part of every system, creating experiences that seem like a friend that knows you, what you like, and what others like, and understands what options are out there for you.

Sunday, June 11, 2017

Jeff Bezos: "Many decisions are reversible, two-way doors. Those decisions can use a light-weight process. For those, so what if you’re wrong? .... If you’re good at course correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive for sure." ([1])

Jeff Bezos: "I would say, a lot of the value that we’re getting from machine learning is actually happening beneath the surface. It is things like improved search results. Improved product recommendations for customers. Improved forecasting for inventory management. Literally hundreds of other things beneath the surface." ([1])

A good summary of Mary Meeker's 2017 report. A key highlight is saturation in smartphones and internet usage. ([1])

New Google AI incubator: "Investment arm aimed squarely on artificial intelligence ... will operate almost like an incubator with a shared workspace for AI startups and mentorship" ([1][2])

Lots of good labeled data (reliable ground truth) is the key to success with AI ([1][2][3][4])

AI in the real world is a lot harder than ideal conditions in part because you see crazy things like robots getting attacked by humans ([1][2])

"The Google [Chrome] ad-blocker will block all advertising on sites that have a certain number of 'unacceptable ads,' according to The Wall Street Journal. That includes ads that have pop-ups, auto-playing video, and 'prestitial' count-down ads that delay the display of content." ([1])

"Designing a [software] library to reduce cognitive load is still the exception, not the rule" ([1][2])

A lesson for bigger companies, investing in the long-term with your researchers, who are often working a few years ahead of what you'll need now ([1])

Wow: "The Melt’s blundering trajectory is instructive ... Entrepreneurs frequently embark on these missions with vast sums of money and a deep belief in technology’s power to solve all problems — which is not always a formula for success .... They were all good people, and they all wanted good things. They just didn’t know anything about running restaurants." ([1])

"The once-hot social network was built on the idea that people would enjoy having anonymous conversations with people close by. That’s a fantastic concept until you remember that anonymous internet person and by definition near you are scary as hell in practice." ([1])

Great teardown of the Juicero, includes some excellent business advice on iterative development and testing your ideas on real customers ([1][2])

"When the US government discovers a vulnerability ... it can keep it secret and use it offensively ... or it can alert the software vendor and see that the vulnerability is patched, protecting the country ... Every offensive weapon is a (potential) chink in our defense." ([1])

On spearfishing attacks: "By a careful design and timing of a message, it should be possible to make virtually any person click" ([1][2])

Schneier on forging voices: "I don't think we're ready for this. We use people's voices to authenticate them all the time, in all sorts of different ways."﻿ ([1])

Facebook says, "We have had to expand our security focus... to include more subtle and insidious forms of misuse, including attempts to manipulate civic discourse and deceive people" ([1][2])

Remarkable and concerning that this is possible: "By accessing accelerometer and gyroscope sensors, the Web-hosted JavaScript measures subtle changes in a phone's angle, rotation, movement speed, and similar characteristics. The data, in turn, can reveal sensitive information about the phone and its user ... [including] the keystrokes being entered" ([1])

Nice high level description here of the difference between what Apple and Google are doing for privacy-preserving machine learning. In brief, Apple adds noise to the data to preserve privacy, but Google learns on the device then sends the updates to the machine learned models back (much like parameters servers in deep learning). The truth is they're probably both doing both, but it's still a good thing to think about. ([1])

Using battery backup to optimize gas power plants by being able to skip the expensive bits for gas turbines, sitting in standby because of lengthy startup times. It's easy and practical, a nice example of low hanging fruit with major impact. ([1])

Good data on the newspaper industry. There's a curious spike in ad revenue from 1980-2000 that isn't matched by subscriptions. ([1])

Jeff Bezos is making journalism profitable: "The Post has said that it was profitable last year — and not through cost-cutting ... The Post has gone on a hiring spree. It has hired hundreds of reporters and editors and has more than tripled its technology staff ... third straight year of double-digit revenue growth ... 'You have to be great at technology. You have to be great at monetization. But one thing I think we’re proving is that if you are, great journalism can be profitable.'" ([1])

How Google took over the classroom, great article, but misses that the failure of iPads was a big piece of this ([1][2])

Duolingo's excellent efforts to help people learn English, which can be a tool for economic or educational advancement ([1])

Almost all cloud workloads right now are not cloud optimized, so the customers mostly moved a system built for fixed hardware resources to the cloud and then run idle a lot rather than redesigning to optimize with dynamically scaling ([1])

Brent Smith and I received the first ever IEEE Internet Computing Test of Time award for our 2003 paper on Amazon's recommender system. In a new article for the IEEE Internet Computing 20th Anniversary Issue, we look back at the last two decades. ([1])

A virtual reality game that succeeds at taking advantage of what it can do well and what it can't to create a fully immersive experience ([1])

Somehow, I missed that Chris Sacca is retiring. Amazing career and influence he had, and impressive to decide to go an entire new direction now. ([1])

In a Stack Overflow survey, what software engineers care about, it's who they work with, what they are doing, and what they learn far more than salary. In the top five items, three are about who you work with and what you learn, one is benefits, and one is commute. But the benefits are complicated -- it's not salary, stock, and bonus -- but the top items all things related to work environment and commute, vacation, and health care. ([1])

Great interview with the CEO of Coursera: "Humility and the ability to listen well are the big things I look for ... If you want to understand people, you need to hear them ... [Also have] ambitious goals to lift the organization up and everybody with it. Setting goals that are ambitious but also achievable is an important skill." ([1])

Great quote from Jeff: "At Amazon, we've had a lot of inventions that we were very excited about, and customers didn't care at all. And believe me, those inventions were not disruptive in any way. The only thing that's disruptive is customer adoption." ([1])

Nice line in Dan Ariely's book Payoff: "If you really want to demotivate people, shredding their work is the way to go, but ... you can get almost all the way there simply by ignoring their efforts." ([1])

Xkcd points out minor changes in methodology yield radical changes in data visualizations of most unusually popular activity in a location ([1])

Translations: I hope others are able to take the content and translate part or all of it into languages other than English for use in more classrooms around the world.

New lessons: New tutorials might teach programming games, working through puzzles or math problems, or perhaps a more traditional computer science curriculum aligned with a particular lesson plan.

Entirely new tutorials: Some of the ideas and techniques -- including the step-by-step learn-by-doing style, live code, informative error messages, and avoiding infinite loops in students' code -- might be useful for others.

The code was designed to be all static, so you can easily create your own version just by editing the files and then putting all the files together on your own server. There is a single JSON file that contains all the lesson content.

If you use the code for anything that helps children learn, I'd love to hear about it (please e-mail me at greg@crunchzilla.com).

Sunday, April 02, 2017

So, you know that prototype we showed you? Turns out AI in real world conditions is hard. ([1][2][3])

Artificial intelligence expert Yann LeCun says, "There have been, on the face of it, impressive demonstrations, [but] those are not as impressive as they look ... They don't have common sense ... One of the things we really want to do is get machines to acquire the very large number of facts that represent the constraints of the real world just by observing it through video or other channels. That’s what would allow them to acquire common sense, in the end." ([1])

Genetic algorithms and neural networks are back. It feels like the 1990s all over again. ([1])

Bringing more novices to AI now is the way to get more experts and advances later ([1])

Nice results from focusing on errors that matter to people, the perceived quality of the system by humans, not theoretical accuracy ([1][2])

Success often comes from trying many things: "Start ... with a hazy intuition or vision ... After a lot of trial and error they get closer and closer to discovering what their idea is ... Seeking novelty instead of objectives is risky — not every interesting thread will pay off — but ... the potential payoffs are higher." ([1])

Research includes people able to do things no one else can, including having data or compute at the frontier beyond what anyone else has done before ([1][2])

6.3M virtual reality headsets sold in 2016, but almost all so far just the cheap toys where you slot your smartphone in to use as the screen
([1][2])

"Total [tablet] sales sinking 15.6%, year on year, with sales of 174.8M units in 2016 compared to 2015's 207.2M" ([1])

For the first time, more people in the US using Netflix than a DVR: "54 percent of US adults reporting they have Netflix in their households compared to the 53 percent of US adults that have DVR" ([1])

The Economist: "Amazon’s heady valuation resembles a self-fulfilling prophecy. The company will be able to keep spending, and its spending will keep making it more powerful" ([1])

"What has surprised AWS as the cloud has evolved ... I don’t think in our wildest dreams we ever thought we’d have a six- to seven-year head start" ([1])

"Yahoo is perhaps most famous for destroying all of its best social properties. From its hideous deformations of Flickr and neglect of Upcoming to its starvation of Delicious and torment of GeoCities users, the company excelled at buying great things and turning them into unusable parodies of themselves. Execs seemed to profoundly misunderstand why people used the sites they bought." ([1])

"Google will account for 78 percent of search ad revenue in 2017, while Facebook will get 39 percent of display ad revenue. Everyone else ... is fighting over the scraps." ([1])

Culture is created by what you publicly reward, not what you say ([1][2][3])

"The problem with bad processes is that they institutionalize inefficiency. They ensure that things will be done the wrong way, over and over and over again" ([1][2])

"Burnout begins when a worker feels overwhelmed for a sustained period of time, then apathetic and ultimately numb .... Workers who used to take the lead on projects grow taciturn during meetings. Top performers start coming in late, leaving early and watch their careers stall ... Burnout is claiming victims at work, and companies aren’t ready to cope" ([1])

A lot of companies have merely medium data, not big data: "Hundreds of enterprises were hugely disappointed by their useless 2 to 10TB Hadoop clusters ... Their data works better in other technologies" ([1])

Saturday, April 01, 2017

This just came out, the book Radical Candor by Kim Scott. It's a good read on managing and focused on people. I'd recommend it if you are a manager or help others manage people.

I'd summarize it by saying it takes a teaching and mentoring approach to management, very much of the school that managers primarily exist to help the people on their team. The advice is both practical and actionable, with specific advice for running 1:1s and meetings, and focused how to encourage conversations where people strive to improve themselves as well as helping others.

Some carefully selected quotes from the book:

"It seems obvious that good bosses must care personally about the people who report directly to them ... And yet ... "

"It turns out that when people trust you and believe you care about them, they are much more likely to accept and act on your praise and criticism, tell you what they really think about what you are doing well and, more importantly, not doing so well, engage in this same behavior with one another ... embrace their role on the team, and focus on getting results"

"When you're the boss, it's awkward to ask your direct reports to tell you frankly what they think of your performance, even more awkward for them than it is for you. To help, I [ask] ... 'Is there anything I could do or stop doing that would make it easier to work with me?' ... It is essential that you ... commit to sticking with the conversation until you have a genuine response. One technique is to count to six before saying anything else, forcing them to endure the silence. The goal is not to bully but to insist on a candid discussion ... Then listen with the intent to understand ... Once you've asked your question and embraced the discomfort and understood the criticism, you have to follow up by showing that you welcome it. You have to reward the candor if you want to get more of it ... Make a chance as soon as possible ... show you're trying."

"If you can absorb the blows, the members of your team are more likely to be good bosses to their employees when they have them ... The rewards of watching people you care about flourish and then help others flourish."

"The ultimate goal of Radical Candor is to achieve results collaboratively that you could never achieve individually ... A culture of guidance ... An exemplary team ... self-correcting quality whereby most problems are solved before you are even aware of them ... Don't start by bossing people. They'll just hate you. Start by listening to them."﻿

Sunday, February 26, 2017

"In addition to making our systems more intelligent, we have to make them more intelligible too ... AI systems to augment human capabilities ... A human-centered approach is more important than ever." ([1])

"Understanding the brain is a fascinating problem but ... separate from the goal of AI which is solving problems ... We don’t need to duplicate humans ... We want humans and machines to partner and do something that they cannot do on their own." ([1])

"Machine learning and reasoning to help doctors to understand patient outcomes -- in advance of poor outcomes ... a great deal of low-hanging fruit where even today’s AI technologies are well positioned to help ... error detection, alerting, and decision support ... could save hundreds of thousands of lives per year" ([1][2])

Not sure how well known this is: "Facebook collects information about pages [you] visit that contain Facebook sharing buttons ... And in case that wasn’t enough, Facebook also buys data about its users’ mortgages, car ownership and shopping habits from some of the biggest commercial data brokers. Facebook uses all this data to offer marketers a chance to target ads to increasingly specific groups of people. Indeed, we found Facebook offers advertisers more than 1,300 categories for ad targeting — everything from people whose property size is less than .26 acres to households with exactly seven credit cards." ([1])

Interesting example for the news industry: "Doubling down on traditional journalism and investing heavily in new ways to deliver it, through smartphone apps, voice-activated speakers and e-readers. The Post’s digital effort has become the envy of the industry, with as many as 80 software engineers, developers and others working alongside reporters and editors to present the news in real time." ([1])

"Bezos has worked to create a culture at Amazon that’s hospitable to experimentation ... developing products customers will actually want to pay for ... experiments start small and grow over time ... a small team to experiment with the idea and find out if it’s viable ... if a team succeeds in smaller challenges, it’s given more resources and a larger challenge to tackle ... prioritize launching early over everything else ... learn as quickly as possible whether an idea that sounds good on paper is actually a good idea in the real world ... getting a product into the hands of paying customers as quickly as possible and taking their feedback seriously ... avoids wasting years working on products that don’t serve the needs of real customers." ([1])

"Many failed ideas have been resuscitated and rebranded as successful products and services, owned and managed by people other than their originators. Behind almost every popular app or website today lie numerous shadow versions that have been sloughed away by time. Yet recognition of the group nature of the enterprise would undermine a myth that legitimizes the consolidation of profit, for the most part, among a small group of people." ([1])

For those of us tracking virtual reality: "While Facebook does not provide sales figures for the $599 Oculus Rift headset ... analysts believe they are slow. One research firm ... estimated the company sold only about 355,000 by the end of last year."﻿ ([1][2][3])

A surprising level of detail here on what software development is like inside of Google. I agree with most of it, and highly recommend reading at least Section 2. ([1][2])

Great blog post summarizing NIPS 2016. Highlights are what wins Kaggle competitions, why deep learning works, latest twiddles to deep learning and reinforcement learning, why dialogs (chat) still doesn't work, and that Baidu has products who's only value is in the data they collect (not direct revenue, just the explore part of explore/exploit, learning how to be more effective). ([1])

Ease of use is badly underrated: "Using TensorFlow makes me feel like I’m not smart enough to use TensorFlow; whereas using Keras makes me feel like neural networks are easier than I realized." ([1])

New paper by Geoff Hinton and Jeff Dean, essentially a very large ensemble of neural networks with sparsity enforced to minimize the computational cost ([1])

Different people we work with in tech tend to have different ideas of what it means to get things done ([1])

"People with different backgrounds bring new information. Simply interacting with individuals who are different forces group members to prepare better, to anticipate alternative viewpoints and to expect that reaching consensus will take effort." ([1])

Meetings are expensive -- a 10 person meeting for an hour costs a few thousand dollars -- and people hate meetings too. Some good reoccurring themes here are to keep meetings small, short, write a tight agenda ahead of time, stay off your laptop and phone, and try to finish early. ([1])

Disappointing game theory tidbit of the day, the Joy of Destruction game shows people enjoy causing harm when they can do it without consequences ([1][2])

Great data visualizations from 538, not just eye candy but convey information quickly ([1])

"Tesla has 1.3 billion miles of car-driving data thanks to its Autopilot-equipped vehicles that are already on the road before competitors in Detroit and Silicon Valley can roll self-driving cars off the lot. It’s a massive competitive advantage." ([1])

Impressive plans from China's space program, probes on the far side of the moon and on Mars in the next four years ([1])

For those interested in education, MIT's popular and excellent Scratch has published a dataset of how people learn computational thinking ([1])

What Code.org has achieved is very impressive: "Trained 50,000 new K-12 computer science teachers ... More than 20 million lines of code have been written by ... more than one million K-12 students ... we expect to dramatically change the demographics of AP Computer Science this year" ([1])

Funny article from The Onion on having too many browser tabs open ([1])

BHAG from Intel: "Intel aims to deliver up to 100x reduction in the time to train a deep learning model over the next three years compared to GPU" ([1])

Deep learning's success is mostly a lot of data paired with an algorithm that can take advantage of a lot of data ([1])

Fun! "A software platform for evaluating and training intelligent agents across the world’s supply of games, websites and other applications ... Agents use the same senses and controls as humans: seeing pixels and using a keyboard and mouse." ([1])

Details on Duolingo's learning algorithms, including that they found what worked best for students using A/B tests ([1])

"The Waffle House Index also stands for something less obvious. It is an indicator of how complex and long supply chains are — for food, for fuel, for power — and of what it takes to plan around infrastructure that can be fragile in unexpected ways." ([1])

Xkcd: "Of course, 'Number of times I've gotten to make a decision twice to know for sure how it would have turned out' is still at 0." ([1])

"Not one, nor two, but five major VC funds reached out about investing in Rocket AI ... The ultimate fake AI company ... AI is at peak hype, and everyone in the community knows it." ([1])

Saturday, December 10, 2016

Cynical, mercenary, and dark, this book aptly serves as an opposing view for any idealism you may have been feeling about Silicon Valley startups or their bigger brethren. Some of us work in technology to make a difference. That is not what you will find in this book.

It is a tale of a startup that wasn't really a startup, three people with no real product acquired after 10 months. It is a tale of sales and personal marketing, spinning unfavorable realities into golden-sounding tales capable of jumping the next hurdle and moving on to the next deal. It is a tale of greed and personal ambition, everything viewed through a Wall Street lens of climbing a hierarchy of wealth and power, some in the world of venture capital, and particularly detailed at Facebook.

Facebook comes out of the book particularly poorly, as if Zuck is a some kind of fickle boy king holding court with his sycophants. During his time at Facebook, the author appears to try to join this clique, only to grow bitter when entry is rebuffed.

Most interesting is the description of Facebook's struggle with advertising revenue, especially after its IPO. As the author describes it, Facebook couldn't figure out how to make the promised revenue. Eventually, in mid-2013 or so, they found a way, not by using data on what people do, but knowing who most people are, which turned out to be particularly important on mobile ("basic targeting like age and gender was a godsend to data-starved marketers ... data-wise, you have a first-party relationship with [only] a few apps"). The real value of Facebook turned out not to be its data on what people are doing, but merely being able to identify most people consistently and willing to exploit that to its fullest.

It helps if you know at least a few of the personalities featured in the book. Paul Graham, Sam Altman, Chris Sacca, Greg Badros, and many others make at least brief appearances, usually to get splattered with the slime that drips from these pages. Many VCs and people at Facebook and Twitter are also mentioned, mostly described as the amoral who's who of the rich and powerful of Silicon Valley.

Like many who got lucky, the author confuses luck with skill. Sure, that pitch meeting went well, but that meeting almost didn't happen. Success often was a result of a chance connection at the right time. In cases where the author angered someone with his arrogance or foolishness, someone should have killed the deal, and might have had they been in a slightly different mood that day. This startup was almost stillborn, barely making it into Y-combinator. The acqui-hire almost didn't happen, almost killed by lack of customer growth and shenanigans by the author. That everything worked out even as well as it did was mostly good fortune.

To his credit, the author realizes some of this in the end. In the acknowledgments, he writes, "Let's be blunt: ours was a relationship of pure convenience, and I exploited you as much as you did me." But he also writes of some he encountered, "In a Valley world awash with mammoth greed and opportunism masquerading as beneficent innovation, you were the only real loyalty and idealism I ever encountered." I'd like to think mammoth greed and opportunism have much smaller representation than idealistic innovation.

Some may call me wishful, but I think pushing for that idealistic world to be true is part of making it true. This book is not going to stop me from thinking that tech companies should be a force for idealistic innovation and promise for the future. At least in my circles, most people I talk with are awash with idealism, a genuine belief that what they are working on can make things better for others. It saddens me to see that the author's perception of the tech industry is so different than my own.

Friday, November 25, 2016

Some of the tech news I found interesting lately, and you might too. Heavy on the comics this time to lighten the mood:

Jeff Bezos: "Good leaders ... seek to disconfirm their most profoundly-held convictions, which is very unnatural for humans ... Anybody who doesn’t change their mind a lot is dramatically underestimating the complexity of the world we live in." ([1])

Amazon is hiring 120k employees just for the holidays. I can't believe how our baby is all grown up. ([1])

On building products: "Keep it extremely simple, or two thirds of the population can’t use your design" ([1][2])

"The problem isn't the users: it's that we've designed our computer systems' security so badly that we demand the user do all of these counterintuitive things." ([1])

Great article on Netflix recommendations, tidbits on the importance of reacting immediately to new data, using immediate intent, freshness (esp. new releases), and perceived quality (difference between online evaluation and offline). ([1])

Opinionated summary of RecSys 2016, and also somewhat of a summary of recommendations and personalization research as of 2016 ([1][2])

Xavier Amatriain on lessons learned from building recommender systems ([1])

YouTube is now using deep learning for recommendations, more than just embeddings, includes a ranker with heavily engineered features ([1])

Pfeffer: "You need to be careful with what you measure, because you are going to get it, and often you don’t really want it." ([1][2])

Obama: "Traditionally, when we think about security and protecting ourselves, we think in terms of armor or walls. Increasingly, I find myself looking to medicine and thinking about viruses, antibodies."﻿ ([1])

Surprising, just set up a hotspot, and the interference from people's fingers moving in the WiFi signal is enough to catch most of the passwords anyone enters while connected ([1][2])

"An entire company’s product line has just been turned into a botnet that is now attacking the United States" ([1][2])

Bit.ly short URLs hid malicious content that was then used to get at Colin Powell's e-mail ([1])

AI guru Andrew Ng: "We're lucky the AI community is very open, and top researchers freely share many ideas and even code. This helps the whole field progress. Hope we can keep it that way." ([1][2])

Love this: "Being able to go from idea to result with the least possible delay is key to doing good research" ([1])

Two new massive labeled open data sets from Google, one for images, one for videos ([1][2])

"Translations that are vastly improved compared to the previous phrase-based production system. GNMT reduces translation errors by more than 55%-85% on several major language pairs" ([1])

Google CEO Sundar Pichai: "Our goal is build a personal Google for each and every user." ([1])

I got a mention in The Guardian for some of my past work: "Greg Linden may not be a household name..." ([1])

Data on what Amazon Echo is actually used for. Mostly playing a song, it appears.﻿ ([1])

Like at the last dot-com boom, there are a bunch of delivery services cropping up with models that don't seem like they're likely to be profitable. Uber, which was in a better position than most to do this profitably, just shut their food delivery service down, which doesn't bode well for the others. ([1])

Current state of virtual reality: "None of these uses are particularly compelling right now, especially given the cost of buying a VR headset. This may change in the future." ([1])

"Giving employees hours, days or even months in which to work without close scrutiny has enhanced productivity instead of harming it"﻿ ([1])

T-mobile's CEO on leadership: "Listen to your employees, listen to your customers, shut the f*** up, and do what they tell you" ([1])

Sunday, August 28, 2016

New Yorker on AI: "A lot of what people are calling 'artificial intelligence' is really data analytics -- in other words, business as usual. If the hype leaves you asking 'What is A.I., really?,' don’t worry, you're not alone .... Intelligent software helps us interact and deal with the ... [information] onslaught ... winnowing an increasing number of inputs and options in a way that humans can’t manage without a helping hand .... A set of technologies that try to imitate or augment human intelligence .... [But] we are a long way from creating virtual human beings ... In the meantime, we're going to have to deal with the hyperbole surrounding A.I." ([1])

Tim O'Reilly: "Humans are increasingly going to be interacting with devices that are able to listen to us and talk back .... [Alexa] demonstrates that conversational interfaces can work, if they are designed right .... Smaller domains where you can deliver satisfying results, and within those domains, spend a lot of time thinking through the 'fit and finish' so that interfaces are intuitive, interactions are complete, and that what most people try to do 'just works'." ([1])

Netflix: "We think the combined effect of personalization and recommendations save us more than $1B per year" ([1][2][3])

"The main reasons cited for using ad blockers include avoiding disruptive ads (69%), ads that slow down their browsing experience (58%) and security / malware risks (56%). Privacy wasn’t the top answer. So Facebook thinks if its can make its ads non-interruptive, fast, [useful,] and secure, people won’t mind." ([1][2])

According to the NYT, Uber lost $1.2B on $2.1B in revenue in H1 2016 ([1][2])

"Amazon reaches new high of 268,900 employees — skyrocketing 47% in just one year" ([1])

Amazon's going hard for Netflix on their key vulnerability, strength of the catalog ([1])

Great example of how Bezos sees failure as just a step toward success, following up on their $170M loss from an expensive Amazon Fire Phone with another (and I think very promising) attempt using existing cheap phones ([1][2])

On education: "A feeling of hopefulness actually leads us to try harder and persist longer -- but only if it is paired with practical plans for achieving our goals, and specific, concrete actions we’ll take when and if (usually when) our original plans don’t work out as expected." ([1])

On management: "We have to give them the space to fail in the short term so they can succeed and grow in the long term ... There is that magical moment when we delegate and allow an emerging leader to grow into their new responsibilities, and they end up being way better at it than we ever were. That’s real management success."﻿ ([1][2])

On teams: "The best teams respect one another’s emotions and are mindful that all members should contribute to the conversation equally ... A shared belief that it is safe to take risks and share a range of ideas without the fear of being humiliated."﻿ ([1][2])

SMBC comic on economists and the golden goose, don't miss the mouseover text: "A physicist would figure out how the Goose was transmuting elements without getting to a high temperature, then use the trillions of dollars to build a really sweet fleet of quadcopters" ([1])

Thursday, June 02, 2016

Bezos: "Every single important thing we’ve done has taken a lot of risk, risk-taking, perseverance, guts, and some have worked out. Most of them have not." ([1])

Bezos: "You need to select people who tend to be dissatisfied ... As they go about their daily experiences, they notice that little things are broken in the world and they want to fix them. Inventors have a divine discontent." ([1])

Page: "Is it going to affect everyone in the world? Very few ... think this way." ([1])

"More than anything else, the rise of the bots signals the death of the mobile app ... The whole app thing didn't really work out." ([1][2])

"As it turns out, the mundanity of our regular lives is the most captivating thing we could share with one another" ([1])

"This is the most demonically clever computer security attack I've seen in years ... insert a nearly undetectable backdoor into the chips themselves" ([1])

"Most Android vulnerabilities don't get patched. It's not Google's fault. It releases the patches, but the phone carriers don't push them down to their smartphone users ... This is a long-existing market failure."﻿ ([1])

"Google, with its tech chops and its control over digital ad delivery, is positioned to do what individual publishers and their associations can’t do on their own, though, by requiring that ads are not obtrusive or annoying — a main reason people choose to block ads."﻿ ([1])

"How quickly cars can learn to do the really hard parts of driving ... navigate congested cities in the pouring rain where humans, pets and rodents run into the road" ([1][2][3])﻿

"Tech firms are plundering departments of robotics and machine learning ... for the highest-flying faculty and students, luring them with big salaries ... The field was largely ignored and underfunded during the 'AI winter' of the 1980s and 1990s, when fashionable approaches to AI failed to match their early promise."﻿ ([1])

The FizzBuzz Tensorflow interview "will probably only make sense to people who have gone through really terrible CS interview processes"﻿ ([1][2])

Remarkable, deep networks trained on artistic style, then used to apply those styles to video ([1])

Saturday, May 14, 2016

Code Monster is a tutorial that has been used by hundreds of thousands of children around the world to learn a little about programming. It's a series of short lessons where each lesson involves reading and modifying a small amount of code. Changes to the code show up instantly, students learning by example and by doing.

The lessons content for Code Monster from Crunchzilla is in a JSON file that can be modified fairly easily to create your own content. By open sourcing Code Monster from Crunchzilla, I hope three things might happen:

Translations. Taking the current content and translating into languages other than English for use in more classrooms around the world.

New lessons and new content. By adding new messages and example code to the JSON lessons file, new tutorials could be created for teaching programming games, working through puzzles or math problems, or perhaps a more traditional computer science curriculum aligned with a particular lesson plan.

Entirely new tutorials. Some ideas and techniques used by Code Monster, such as how Code Monster provides informative error messages, how it does live code, or how it avoids infinite loops in students' code, might be useful for others creating web-based coding environments.

Code Monster from Crunchzilla has been used in computer labs and classrooms around the world. One of the most common requests is translations into languages other than English. Now that the code is open source, I hope that makes it easier for translated and modified versions to get in front of even more children.

If you use the code for anything that helps children learn computer programming, I'd love to hear about it (please post a comment here or e-mail me at greg@crunchzilla.com).

Saturday, April 02, 2016

"We simply don't know how to securely engineer anything but the simplest of systems" ([1])

Impressive at their scale: "Facebook ... releases software ... three times a day" and makes configuration changes "thousands of times a day... every single engineer can make live configuration changes." ([1])
﻿

For those of us tracking virtual reality, a detailed review of the Oculus Rift ([1]), a review of Hololens ([2]), and a fun TED talk motivating augmented and virtual reality ([3])

For disk to be the new tape "custom disk designs uniquely targeting cold storage" are required that are "much larger, slower, more power efficient and less expensive." ([1]) Related, Google seeks new disk designs ([2])

Lessons from building AWS, including automate everything and favor primitives over frameworks ([1])

In the AWS service terms: "However, this restriction will not apply ... [when] human corpses to reanimate and seek to consume living human flesh, blood, brain or nerve tissue." ([1])

Google says, "With multi-homing ... failover, recovery, and dealing with inconsistency ... are solved by the infrastructure, so the application developer gets high availability and consistency for free and can focus instead on building their application" ([1][2])

Remarkably successful contest: "The winning team exceeded the power density goal for the competition by a factor of 3 ... Some of us at Google didn’t think such audacious goals could be achieved." ([1])

Netflix's catalog has dropped to 5,532 titles from 8,103 titles in about two years ([1][2])

"The James Webb Space Telescope will be a major advance ... primary mirror will be 50 times [larger] ... eight times the resolution" ([1])

"The price of planetary insurance, it turns out, isn’t all that high." ([1][2])

Teaching math: "In most people’s everyday lives ... what [people] do need is to be comfortable reading graphs and charts and adept at calculating simple figures in their heads ... Decimals and ratios are now as crucial as nouns and verbs." ([1])

He's the "‘seagull of science.’ He used to fly in, squawk, crap over everything, and fly away."﻿ ([1])

Good answer to the question, "What are the most important things for building an effective engineering team?" ([1]) Related, similar advice from Amit Singh ([2][3])

An old Amazon.com office map from early 1997 (back when Amazon only sold books, "Earth's Biggest Bookstore"). My "office" was a card table in a kitchen. ([1])

What If comic: What would happen if you tried to squeeze all the water going over Niagara Falls into a straw? It's worse than you'd think. ([1])﻿

Saturday, March 05, 2016

The world is substantially different than the last time this happened. In particular, there's more computing power available in our smartphones than the most powerful graphics workstations had back in the 1990s. Google Cardboard and others take advantage of that, using a smartphone and little else for a quick-and-dirty virtual reality experience.

But, for a product to appeal to a broad market -- to get beyond early adopters with disposable income seeking to show something cool to friends a couple times -- it needs to survive the harsh judgement of busy people. It isn't enough for virtual reality on expensive dedicated hardware to mostly work. The experience will have to wow repeatedly at a price people like.

So, Daniel and I have another bet: "Virtual reality hardware (not counting cardboard) will not sell more than 10M units/year worldwide before March 2019." I'm saying it won't. Daniel says it will. Loser donates $100 to the winner's choice of charity.

Daniel already posted his side of the bet. In brief, he thinks three years will be enough time for someone to get it right.

I think that mainstream adoption of dedicated hardware for virtual reality requires breakthroughs in usability and price that are too difficult to achieve in the three year time frame. The experience just isn't good enough yet for it to be anything other than a toy for early adopters. Current virtual reality hardware is bulky, expensive, not fully immersive, and not addictive or compelling beyond the initial wow. I expect even the next generation will just be a niche market (low million units per year) until we see major developments on price, form factor, and quality of the experience.

There are several wild cards here. For example, it is possible that much cheaper units can be made to work. It's possible that someone discovers very carefully chosen environments and software tricks fool the brain into fully accepting the virtual reality, especially for gaming, increasing the appeal and making it a must-have experience for a lot of people. As unsavory as it is, pornography is often a wild card with new technology, potentially driving adoption in ways that can determine winners and losers. A breakthrough in display (such as retinal displays) might allow virtual reality hardware that is much cheaper and lighter. Business use is another unknown where virtual reality could provide a large cost savings over physical presence. I do think there are many ways in which I could lose this bet.

Like Daniel, I'll add some constraints to make my side of the bet even harder. I'd be surprised if dedicated virtual reality hardware sells more than 10M total over all three years. I'd also be surprised if virtual reality using smartphones (like Google Cardboard) goes beyond a toy, so, is used regularly by tens of millions for gaming, education, or virtual tourism.

And, like Daniel, I expect virtual reality to be big eventually, am frustrated by our current computing limitations, and think we should work to have much better from our computing devices today.

Saturday, February 27, 2016

In 2012, Professor Daniel Lemire and I bet $100 over the question of whether tablets would replace PCs.

Specifically, the bet was, "In some quarter of 2015, the unit sales of tablets will be at least twice the unit sales of traditional PCs, in the USA." Loser donates $100 USD to the charity of the winner's choice.

It's 2016, and tablet sales went far higher than I ever expected, approaching PC sales, roughly 60M/year units for both tablets and PCs in the US. But tablet sales seem to have peaked, with Q4 2015 unit sales worldwide actually 14% lower than the previous year, which is worse than the 8% decline in PC sales.

There are other surprises. One of my concerns was that a very cheap tablet would dominate the market, and Amazon did come out with a $50 tablet that got relatively good reviews and nearly tripled Amazon's market share on tablets. There hasn't been enough time yet to see what happens with very cheap tablets, but tablets this cheap are a different category than the tablets that were around in 2012.

Another concern at the time was hybrid tablets, so tablets with detachable keyboards that function a lot like laptops, and whether they'd blur the line between PC and tablet. Hybrid tablets have done very well -- a major category in tablets -- and look likely to continue to grow over time.

The last concern at the time was whether tablets could thrive despite the pressure from increasingly larger and more powerful mobile phones. That seems to have been the biggest issue. Phablets are getting as large as early tablets, and tablets that try to be much bigger than a smartphone proved too unwieldy and sold poorly. After all, who needs a tablet when you've got a mobile that's almost as large?

The broader question in the bet was whether people would stop using PCs. PC sales have been in decline, though the pace of that decline has slowed recently. What seems to be happening is that people are continuing to use multiple devices, which was a visible trend back in 2012.

A phone is great when you want to do something quickly on the run. A bigger screen is good when you need to do a lot of reading. A keyboard, mouse, and large screen become useful when you're producing instead of consuming. If you need to do all of these, there's no reason to only have a phone, only a tablet, or only a PC. Instead, people often have all three and more.

Even though I technically won this bet, I want to congratulate Daniel Lemire on this getting much closer than I ever expected. I also admire the bravery he had to take the bet, especially with such favorable terms, and appreciate what I learned from this. The terms were that the loser donate $100 to the charity of the winner's choice, and I'd like to match the donation. Daniel and I will both be donating $100 to the Wikimedia Foundation, which runs Wikipedia.

Sunday, January 17, 2016

"Big ideas emerge from spills, crashes, failed experiments and blind stabs .... As people dredge the unknown, they are engaging in a highly creative act .... the habits that transform a mistake into a breakthrough" ([1])

Lots of details on recommendations, personalization, and experimentation at Netflix in a new ACM paper ([1])

Fun and interesting Slate article on how Facebook selects posts for the news feed ([1])

New paper claims the filter bubble for news is much stronger in what people self-select and on social media than in search and recommendations ([1])

"Bayesian program learning (BPL) framework, capable of learning a large class of visual concepts from just a single example and generalizing in ways that are mostly indistinguishable from people" ([1][2][3])

NIPS 2015 paper on problems that accumulate in machine learning systems, such as dependencies between features, dependencies between models that build off each other, and complicated and fragile data preprocessing ([1])

"Should they teach [self-driving] cars how to commit infractions from time to time to stay out of trouble?"﻿ ([1])

Wal-mart is doing poorly against Amazon, which is surprising, I think ([1])

Good article on product management. I particularly like the points that most products fail (so you should expect to experiment, adapt, and iterate) and that a good product is about experiences not features ([1])

"People keep mentioning how different things are to the period just before the AI winter"﻿ ([1])

"Smartwatches still have a long way to go in terms of proving their usefulness, necessity, and style" ([1])

"CYA security: given the choice between overreacting to a threat and wasting everyone's time, and underreacting and potentially losing your job, it's easy to overreact." ([1])

Saturday, January 02, 2016

I've been working on a couple educational projects since Google, SwipeLingo and Javascript Notebook.
SwipeLingo is a quick matching game for touchscreens. Javascript Notebook is a tool for writing coding tutorials, exercises, and examples.

I'm unable to fully finish them and get them exactly where I wanted them before starting at Microsoft. But I'm launching anyway in case they or the ideas in them are useful to others.

SwipeLingo is a game-with-a-purpose, a quick matching game that is both fun and helps with memorization like flash cards do. There are example games — particularly interesting is Chinese numbers, where you learn the characters pretty quickly after starting with wild guessing — and it's also easy to create your own. I was motivated to create SwipeLingo by loving Duolingo but wanting the vocabulary memorization in it to be more fun, and also wanting to try to build a non-native touch web app game that works equally well across desktop, laptop, tablet, and phone.

Javascript Notebook tries to make it easy to write and share coding tutorials, coursework, examples, exercises, and experiments. It was heavily motivated by Stanford's CS101 class and their content. Here are some examples: "Getting Started", "Introduction to Programming", "What You Can Do". It's a bit like a simple Javascript-only IPython Notebook in feel, but runs entirely in the browser, requiring no configuration or set up, just write and share. Others can modify the code, run it, and save and share their own copies.

Please let me know if take a look and have any comments or suggestions. And please tell others who might be interested about them too!

Monday, December 28, 2015

I'm joining Microsoft! I'll be part of the excellent Analysis and Experimentation team, helping people learn from data. I'm excited!

I've been geekingoutwithbigdata from before data science was a thing and before being a geek ever could be considered a compliment. Fortwodecades, I've enjoyed looking at the paths people take online, where they find success and where they become annoyed, and how changes can help more find success.

Sometimes this is prioritizing things people like and find useful.
Sometimes it is changing or eliminating things that, despite the good intentions of the developers and designers, don't work for people.
Sometimes it is anonymously sharing things that only some people found with others who haven't found it yet.
And sometimes it is having humility about being able to guess what will work and deciding to try many things to discover what actually does work.

If you're at Microsoft, whether an old friend, a team looking to talk about recommendations, personalization, data science, and experimentation, or just looking to chat, please get in touch! I'd love to hear from you.